9 research outputs found
Central neural circuitry mediating courtship song perception in male
Animals use acoustic signals across a variety of social behaviors, particularly courtship. In Drosophila, song is detected by antennal mechanosensory neurons and further processed by second-order aPN1/aLN(al) neurons. However, little is known about the central pathways mediating courtship hearing. In this study, we identified a male-specific pathway for courtship hearing via third-order ventrolateral protocerebrum Projection Neuron 1 (vPN1) neurons and fourth-order pC1 neurons. Genetic inactivation of vPN1 or pC1 disrupts song-induced male-chaining behavior. Calcium imaging reveals that vPN1 responds preferentially to pulse song with long inter-pulse intervals (IPIs), while pC1 responses to pulse song closely match the behavioral chaining responses at different IPIs. Moreover, genetic activation of either vPN1 or pC1 induced courtship chaining, mimicking the behavioral response to song. These results outline the aPN1-vPN1-pC1 pathway as a labeled line for the processing and transformation of courtship song in males
Angular velocity integration in a fly heading circuit
Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in the Drosophila melanogaster central complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation
Post hoc immunostaining of GABAergic neuronal subtypes following in vivo two-photon calcium imaging in mouse neocortex
GABAergic neurons in the neocortex are diverse with regard to morphology, physiology, and axonal targeting pattern, indicating functional specializations within the cortical microcircuitry. Little information is available, however, about functional properties of distinct subtypes of GABAergic neurons in the intact brain. Here, we combined in vivo two-photon calcium imaging in supragranular layers of the mouse neocortex with post hoc immunohistochemistry against the three calcium-binding proteins parvalbumin, calretinin, and calbindin in order to assign subtype marker profiles to neuronal activity. Following coronal sectioning of fixed brains, we matched cells in corresponding volumes of image stacks acquired in vivo and in fixed brain slices. In GAD67-GFP mice, more than 95% of the GABAergic cells could be unambiguously matched, even in large volumes comprising more than a thousand interneurons. Triple immunostaining revealed a depth-dependent distribution of interneuron subtypes with increasing abundance of PV-positive neurons with depth. Most importantly, the triple-labeling approach was compatible with previous in vivo calcium imaging following bulk loading of Oregon Green 488 BAPTA-1, which allowed us to classify spontaneous calcium transients recorded in vivo according to the neurochemically defined GABAergic subtypes. Moreover, we demonstrate that post hoc immunostaining can also be applied to wild-type mice expressing the genetically encoded calcium indicator Yellow Cameleon 3.60 in cortical neurons. Our approach is a general and flexible method to distinguish GABAergic subtypes in cell populations previously imaged in the living animal. It should thus facilitate dissecting the functional roles of these subtypes in neural circuitry
Recommended from our members
A Drosophila computational brain model reveals sensorimotor processing.
Acknowledgements: We thank members of the Scott laboratory for their contributions to the experimental and model design, data analysis and manuscript preparation. We thank C. Liu, M. Levy and G. Agarwal for their feedback on the initial development of the computational model. We thank the laboratories of C. Ribeiro and R. Wilson for Flywire proofreading contributions. We thank the entire Flywire community for their contributions to the proofreading of the Drosophila connectome. We thank M. Sorek for assistance with Flywire community management. We thank R. Lu, T. Macrina, K. Lee, J. A. Bae, S. Mu, B. Nehoran, E. Mitchell, S. Popovych, J. Wu, Z. Jia, M. Castro, N. Kemnitz and D. Ih for alignment and segmentation of the FAFB electron microscopy volume and registration to the original FAFB electron microscopy dataset. We thank D. Bock and E. Perlman for sponsorship of partial proofreading and registration service. We thank F. Collman, C. Schneider-Mizell, C. Jordan, D. Brittain and A. Haligeri for CAVE development and maintenance. We thank K. Kuehner, O. Ogedengbe, J. Gager, W. Silversmith and R. Morey for Neuroglancer development, tools and Codex development. We thank S. Seung for his suggestions and contributions to Flywire. N.S. is funded by the Carl Angus DeSantis Foundation. This work was supported by National Institutes of Health (NIH) awards R01DC013280 (K.S.), NIH F32DK117671 (G.R.S.), NIH F32DC018225 (P.S.) and RF1NS121911 (S.H. and A.M.S.). Flywire is supported by NIH BRAIN Initiative grants MH117815 and NS126935 to M.M. and S. Seung. Additional proofreading and infrastructure was supported by Wellcome awards 203261/Z/16/Z and 220343/Z/20/Z to G.S.X.E.J., and NIMH BRAIN Initiative award 1RF1MH120679-01 and NSF NeuroNex award DBI-2014862 to D. Bock and G.S.X.E.J. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5-a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6-10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations